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Do Sex, Age, and Homeownership Affect How Often People Interact with Their Neighbors?

28 Dec

Abstract

This article analyzes the extent to if and how sex, age, and homeownership affect how often people interact with their neighbors within the Neighbor4Neighbor (N4N) boundaries in an Elkhart, Indiana neighborhood.  In this study, surveyors went door-to-door administering a “Quality of Life” survey to the residents inside the neighborhood.  The sample contained 120 different residential participants, which lived inside the neighborhood, and were at least 18 years of age.  The survey was designed to test and compare the residents level of social cohesion to the other residents level of social disorganization.  In order to compare these two characteristics, the final results were calculated using a bivariate means comparison analysis.  The results in this study conclude that men engage with their neighbors more than women, younger cohort socialize with their neighbors more than older cohorts, and, like many other past studies have shown, homeownership increases social cohesion inside neighborhood boundaries.      

Introduction

Throughout the world, individuals acquire friends, ties with other individuals, and/or neighbors from the early days of childhood all the way to the end of the line in a retirement home.  Social interaction is just as important as the human heart due to the fact that they both drive humans as a species.  Social interaction is the center of gravity for adolescents, teens, and adults worldwide.  As one ages and makes their way out of college into the workforce, your social interactions within the workforce grow but those outside of the workforce often dwindle. After years of work and sacrifices, which are often made for family and homeownership, workforce interactions often dwindle.  At that time, interactions with neighbors who are like you or around you begin to foster more than ever.  Oh (2004:66) assess that “Homeownership is a major indicator of economic well-being at the household level because a home is an investment and a significant financial asset to most homeowners.  Furthermore, such economic interest in home equity leads to neighborhood attachment among homeowners who share an important economic fate with their neighbors.”  It has been noted in several sociological studies that the elderly tend to live longer and have happier lives when neighborhood interactions take place.  Nattavudh Powdthavee (2007:9) asserts that “The older cohort (aged over 30), on the other hand, tend to spend more time talking to their neighbors than the younger cohort (20 and under).”  For this research paper, I plan on examining and answering if to what extent does sex, age, and home ownership affect neighborhood interaction levels.” 

Literature Review

There is substantial evidence that suggest that social relationships promote happiness for individuals in society.  Many studies have found the level of reported life satisfaction to be high among those who are married, women, white, the well-educated, the self employed, the retired, and those occupied with home duties (Powdthavee 2007; Rohe and Stewart 1996).  A positive sense of worth is key to acquiring and fostering strong community ties.  Those who lack these characteristics are often viewed as having what sociologist refer to as social disorganization.  Guest, Cover, and Ross (2006:365) define social disorganization as “a condition of few social key ties, high community anomie, little empathy toward others, and weak social control.”  As a result, less neighborhood ties create more social disorganization (Bellair 1997).  There is substantial evidence that people with strong social ties tend to report on average higher levels of happiness and satisfaction than others (Powdthavee 2007; Guest, Cover, and Ross 2006).  The work that I present in this study will help shed additional light on how sex, age, and homeownership act as functions that eliminate social disorganization and create strong, positive community ties.  Basing on previous findings, I propose that different sexes (male/female) have unequal neighborhood ties.  I made this prediction because Nattavudh Powdthavee (2007:9) notes that “Among adults of working age, 43% of men and 50%, of women say that they meet up with their friends on a daily basis.  The figure is slightly smaller when it comes to socialization with neighbors; 33% and 39% for men and women, respectively.  Interactions with friends and relatives take place more often among the young.  The older cohort (aged over 30), on the other hand tend to spend more time talking to their neighbors than younger cohorts (30 and under).”  

Neighbors getting together and socializing with one another plays an important role in establishing effective social controls (Bellair 1997).  When it comes to socialization and satisfaction, Nattavudh Powdthavee (2007:6) notes that “When it comes to life satisfaction there is a pronounced U-shape in age, minimizing at around early 40s.”  Based on these findings, I propose that as an individual’s age increases, the more neighborhood ties they acquire.  I believe that this is the case because individuals with rich networks of active social relationships, that do not include people living in the same household, tend to be happier with their lives (Powdthavee 2007; Oh 2005).  Social disorganization often creates unequal neighborhood ties because residents, who rent, are often less involved in neighborhood activities than those residents who own their home (Rohe and Stewart 1996).  

Oh (2004:65) concludes that “Homeownership is positively associated with two neighborhood attachment measures: neighborhood satisfaction and neighborhood sentiment.”  Neighborhood satisfaction is often more valued by those residents who have children and own their own homes.  “Homeowners will come in contact with other homeowners who will further add to an individual’s knowledge base.  The net result may be a snowball influence in which the overall context leads to more information than would be available on the basis of simply knowing the individual types of persons in the area” (Guest, Cover, and Ross 2006:375-76).  Based on these findings, I propose that homeownership increases an individual’s neighborhood ties.  I made this prediction because Guest, Cover, and Ross (2006:365) assert that “Homeowners build strong ties to protect a major form of their wealth.  Knowing and interacting with neighbors is a rational means of protecting one’s investment in home and/or children.  For those owning homes, knowing about neighbors may be crucial to certainty that their properties will be protected when they are away.”  Rohe and Stewart (1996) also suggest that knowing about your neighbors may be as crucial to protecting one’s investment as actually interacting with them.  “People who know one another often work out interpersonal agreements for achieving desired goals.  They are made possible by the fact that the people involved are personally acquainted.  Persons who remain strangers will be systematically less likely to be willing to participate in such mutual agreements” (Bellair 1997:681).  These mutual agreements act as a bridge that further strengthens community ties which also helps community residents protect their financial investment inside of the community.    

Methods/Data Collection

This “Quality of Life” survey involved data collection that took place in February and March of 2012.   This project was commissioned by The City of Elkhart and was conducted by Indiana University South Bend student researchers who worked in pairs as they went door-to-door asking one adult resident whom they encountered, from each home, to participate.  This survey was conducted for the N4N (Neighbor4Neighbor) Homeowners Association which is located in Elkhart, Indiana.  The surveyors had specific N4N boundaries to research and they consisted of North Vine Street on the East (West side of the street only), Cedar Street on the North (both sides of the street), West Boulevard North on the West (East side of the street only), and Strong Avenue on the South (both sides of the street).  When conducting the survey, no homes were skipped, every door was knocked on, and if a home was abandoned and/or vacant, it was recorded as such.  This initial visit yielded one of six possible results: 1. Completed survey; 2. Refusal to participate; 3. Request that we come back later, at a better time, or when an adult resident was home; 4. No answer; or 5. Did not speak English. 6. Home was vacant. While surveying, if no one was home, or the resident asked the surveyors to come back at another time, the surveyors would make a note of this, move on to the next residence, and then return that day or revisit at a later date, often multiple times if necessary.  Residents, who fell within the N4N boundaries and were at least 18 years old, were asked to participate in short 10 to 15 minute survey which was administered by the paired up surveyors.

133 of the 345 homes that were visited had residents that were not home, or they did not answer the door during subsequent visits.  To ensure consistency and accuracy, the survey was only administered to people who speak English because only 4 of the 20 surveyors spoke fluent Spanish.  There was only one resident who did not speak English.  In total, the surveyors visited 345 homes and they completed 120 surveys.  Of those surveys that were not completed: 55 residents refused participation, 133 were not home or never answered the door, 27 of them continuously asked us to come back later, and 7 homes appeared vacant. If we compare the number of completions with the number of refusals, the completion rate is 55.8 percent (120/215).  These type of surveys are not designed to reached or even set out to survey every member of the neighborhood.  Whereas, the overall goal is to survey a sizeable and representativesubset of the neighborhood. The N4N sample represented a sizeableportion of that neighborhood in which 34.8 percent of households were represented in the survey, by our estimation (120/345).

Of those residents who did agree to participate, 40.8% were male and 59.2% were female (49 males & 71 females).  The ages of the participants ranged from 18 to 94 years old, yet 25% of the respondents were 74 years or older.  The majority of participants were homeowners, with 91.7% owning their homes compared to the 8.3% that did not own their homes (110 owned & 10 rented). (see Table A1)

For this study, I wanted to show how my independent variables, which are ‘sex’, ‘age’, and ‘homeownership’, affect the extent to how often these residents interact with their neighbors which was my dependant varible. For my nominal independent ‘sex’ variable, I used the survey question: Gender (mark down but don’t ask): (1) Male, (2) Female.  For my second ordinal independent ‘age’ variable, I used the survey question: In what year were you born?  The respondents gave the year in which they were born, instead of their numerical age.  Their ages ranged from 18 to 94 years of age.  Because this time fame was too broad, I decided to recode this variable.  I grouped the age categories into 10 year increments starting at 30 years of age so that each decade could be examined as individual ages increased.  For my third nominal independent ‘homeownership’ variable, I used the survey question: Do you or your family own or rent your home?

I switched the number categories when it came to my ordinal dependent variable because they seemed counter intuitive to how number scales actually work.  The survey associated the number 1 with often, 2 with sometimes, 3 with rarely, 4 with never, 5 with don’t know and 6 with refused.  These categories seemed opposite of a normal scale where the lower the number the less it’s accepted or approved.  I decided to switch the numbers around and I also grouped the never(s), don’t know(s), and refused together because all three categories are saying that the respondent doesn’t engage with their neighbors.  The new categories were relabeled so that 4 became often, 3 was sometimes, 2, was rarely, and 1 was never (with the don’t know(s) and refused clumped in).  For the actual dependent variable, I combined 5 questions that each had a possible 7 answers per question.  I decided to combine these questions because it would be easier to find a more accurate representation of how many people, how often, and, more importantly, do males or females participate in those activities with their neighbors.  I used the survey questions:

  1. 1.      How often do you visit in each other’s’ homes or on the street?
  2. 2.      How often do you help each other with chores or errands?
  3. 3.      How often do you borrow or loan household items (such as food or tools)?
  4. 4.      How often do you discuss things going on in your neighborhood?
  5. 5.      How often do you discuss personal matters?                                                                                                          Would you say (1) Often, (2) Sometimes, (3) Rarely, (4) Never, (5) Don’t Know (don’t ask), or Refused (don’t ask)

 

In the end all 35 answers categories were combined to create one question which was: How many neighbors do you know by name?  This combined question allows for a better interpretation of how often males and/or females engage with their neighbors.  Using these independent and dependent variables, I proposed three different hypotheses.  My first hypothesis was that women are more likely then men to engage with their neighbors (see Table B1). My second hypothesis was that homeowners will engage with their neighbors more than non-homeowners (see Table B2), and my third hypothesis was that older individuals will engage with their neighbors more than younger individuals (see Table B3).

 

 

Results

Table. A1.  Demographics and Univariate Frequency Distributions

Variable Variable   Category Valid%
Sex    
      Male 40.8
      Female 59.2
Homeownership    
      Own 91.7
      Rent 8.3
Age    
  18-30 years 15.0
  31-41 years 20.8
  42-52 years 19.2
  53-63 years 20.0
 

Number of Neighbors

Know by Name

74 years or older

 

0-35   known

36-70   known

71-105 known

106-140 known

25.0

 

32.5

34.2

30.0

3.3

     

 

There were more female participants than male participants in this study.  Out of the 120 respondents in this survey, 71 (59.2%) were female and 49 (40.8%) were males.  The mode for these two groups favored females because there were 22 more females than males.  110 (91.7%) of the participants owned their homes, compared to the 10 (8.3%) participants who rented.  The mode for these two groups favored homeowners because there were 100 more people who owned their homes in the N4N boundaries.  The respondents ranged in age from 18 to 94 years old.  The first category contained 18 respondents (15.0%), the second contained 25 respondents (20.8%), the third contained 23 respondents (19.2%), the fourth contained 24 respondents (20.0%), and the fifth category contained 30 respondents (25.0%).  The mode favored the fifth age group which consisted of the 74 years or older participants because most respondents fell into this range.  Out of the 120 respondents that were surveyed, 39 respondents (32.5%) knew 0 to 35 of their neighbors, 41 respondents (34.2%) knew 36 to 70 of their neighbors, 36 respondents (30.0%) knew 71 to 105 of their neighbors, and only 4 respondents (3.3%) knew 106 to 140 of their neighbors.  Using a bivariate means comparison analysis, I compared each of my independent variables against my dependant variable to analyze and calculate if and how each of these variables were related.

Hypothesis 1:Women are more likely then men to engage with their neighbors. (see Table B1)

For my first hypothesis concerning gender and neighbors know by name, the bivariate analysis did not support my initial hypothesis.  The means comparison results suggest that men are more likely than women to engage with their neighbors.  When comparing women to men, it is important to note that the men had a higher mean of 55.6 compared to the women’s 50.8.  As a result, my hypothesis was not supported because men engaged more than women.

Table B1: Means Comparison Between the Number of Neighbors Known by Name and the Respondents Sex

 

Respondent’s   sex

Mean

N

Std.   Deviation

Male

55.5714

49

34.18881

Female

50.7746

71

30.88791

Total

52.7333

120

32.22263

 

 

 

Hypothesis 2: Homeowners will engage with their neighbors more than non-homeowners. (see Table B2)

My results concerning the connection between homeownership and neighbors known by name was considerably different. For my second hypothesis, the bivariate analysis does support that homeowners engage with their neighbors more than non-homeowners.  When comparing the 110 homeowners to the 10 non-homeowners, those who owned their home on average had a mean of 55 compared to 28 for those who did not own their home.  Thus my hypothesis was supported by these results.

Table B2: Means Comparison Between the Number of Neighbors Known by Name and Do You or Your Family Own or Rent This Home

Do you or your family own or rent this home?

Mean

N

Std.   Deviation

Own

55.0000

110

30.82594

Rent

27.8000

10

38.29650

Total

52.7333

120

32.22263

 

 

Hypothesis 3: Older individuals will engage with their neighbors more than younger individuals.  (see Table B3)

For my third hypothesis which compared gender and neighbors know by name, the bivariate analysis did not support my initial hypothesis. The means comparison results suggest that individuals, who are 42 years of age or older, do not engage with their neighbors more than individuals who are 41 and under.  When comparing age ranges, it is important to note that out of the 120 respondents, most respondents were between the ages 31-41.  This group had the highest number of respondents known by name with a mean of 59.04.  The next closet age group was those respondents whose age ranged from 74-94, but it is important to note that that age range is 20 years compared to the other groups that had a 10 year range.  Every other age group had fewer participants who engaged with their neighbors and significantly lower means than respondents who were between the ages of 31-41 years old.  As a consequence, my hypothesis was refuted.

 

Table B3: Means Comparison Between the Number of Neighbors Known by Name and the Year the Respondent was Born

 

year   born recode

Mean

N

Std.   Deviation

18-30   year olds

32.5000

18

27.28553

31-41   years old

59.0400

25

34.64881

42-52   years old

53.8696

23

38.93737

53-63   years old

53.6250

24

28.61257

74-94   years old

58.0333

30

26.68395

Total

52.7333

120

32.22263

 

Conclusion

This paper explored the age old question: Do sex, age, and homeownership affect how often people interact with their neighbors?  Out of the three bivariate means comparison analysis test that I ran, only one of my original hypothesizes were confirmed.  I originally questioned whether or not residents’ investments in the neighborhood affected their perceptions of their neighborhood and, overall, my findings do support this connection.  When it comes to a financial investment, homeownership increases neighboring because homeowners are more likely to have and/or know other neighbors more than those who are non-homeowners.  The results in this study also conclude that men engage with their neighbors more than women, younger cohort socialize with their neighbors more than older cohorts, and, like many other past studies have shown, homeownership increases social cohesion inside neighborhood boundaries.  Oh (2004:65) writes “in the case of neighboring, social cohesion/trust among community residents is a symbol of neighborhood attachment because it arises from shared norms and values among local neighbors.”  When comparing these shared values and norms, it is important to note that males do socialize, engage with, and know more neighbors than their female counterparts.  This fact may have only been present in this small geographic neighborhood which contains 345 houses; however, these findings do show that men are just as easy to socialize with as women.  This is important because evidence has shown that people with strong social ties tend to report on average higher levels of happiness and satisfaction than others (Powdthavee 2007; Rohe and Stewart 1996).  Before conducting my research, I believed that age didn’t have an effect on the level of neighborhood interaction because both young and old individuals find a sense of comfort in others who are likeminded, have the same values, live in proximity to each other, and close in terms of age.  This study showed that age is just a number and it doesn’t really affect social cohesion inside neighborhood boundaries.  This was a solid, straightforward research project, but, this same research is not without its own limitations.  For one thing, the findings from this small Elkhart, Indiana neighborhood can’t be generalized to a larger population.  More importantly, because I examined the characteristic of homeowners compared to those who were non-homeowners, it must be noted that non-homeowners were not equally represented in the sample.  If I were to replicate this study, I would choose a more diverse neighborhood in terms of homeownership.  The age range categories were as close to equal representation as possible.  If one examined the current state of the American economy, __% of the population is between 74 and 90 years of age.  In many survey structured studies, men are often underrepresented; however in our study, the level of participating men was high with 49 participants compared to the women’s 71 participants.  This research helped to shed awareness on social cohesion and social disorganization in a small Elkhart neighborhood.  I encourage all researchers to replicate this initial study just to see if this small sample is close to being as representative as a larger sample would be.  This initial research sheds light into the room, but the door must be all the way open, so that we can get a full picture of how the larger population is affected when it comes to sex, age, and homeownership in the context of  neighborhood interactions.

 

References

Bellar, Paul E. 1997. “Social Interaction and Community Crime: Examining the Importance of Neighbor Networks.” Criminology 35(4): 677-704.

Guest, Avery M., Jane K. Cover, Ross L. Matsueda, Charis E. Kubrin 2006. “Neighborhood Context and Neighboring Ties.” City & Community 4: 363-385.

Oh, Joong-Hwan 2004. “Race/Ethnicity, Homeownership, and Neighborhood Attachment.” Race and Society 7: 63-77

 

Powdthavee, Nattavudh2008. “Putting a Price Tag on Friends, Relatives, and Neighbours: Using Surveys of Life Satisfaction to Value Social Relationships.” Journal of Socio-Economics 4: 1459-1480.

Rohe, William M. and Leslie S. Stewart. 1996. “Homeownership and Neighborhood

Stability.” Housing Policy Debate 7(1): 37-75.

 

 

 

 

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